---
title: "Market Data"
output:
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: scroll
    source_code: embed
    theme: journal

---

```{r setup, include=FALSE}

library(quantmod)
library(xts)

#Graphing Packages
library(ggvis)
library(ggplot2)
library(plotly) #Interactive Graphs
library(flexdashboard) #Dashboard Layout
library(plyr)
library(dygraphs) #Used for Time Series
library(ggthemes)
library(ggfortify)


#Treasury Data
library(ustyc)

#Reshape Data
library(reshape2)

#Data Tables
library(DT)

#Forecast
library(forecast)

#Shiny Web App
# library(shiny)
library(webshot) #Takes screenshots of webpages

#Dplyr
library(dplyr)

# XLSM
# library(xlsx)
# install.packages(c("reshape2", "DT", "forecast", "shiny", "webshot", "dplyr", "xlsx", "shiny", "webshot", "dplyr", "xlsx"))
```


```{r Import Treasury Data}

treasury_data <-
  read.csv('C:\\Users\\Noe_N\\OneDrive\\Market Data\\market_dashboard\\market_data\\Treasury.csv')

# Scrub Column Names ----
colnames(treasury_data) <- gsub("X", "",  colnames(treasury_data)) # remove x
colnames(treasury_data) <- gsub("\\.", "", colnames(treasury_data)) # remove "."
colnames(treasury_data) <- gsub("Rate", "", colnames(treasury_data))
colnames(treasury_data) <- gsub("Yr", "", colnames(treasury_data))

# Update Data Types ----

#TODO: Update code to be more iterative
treasury_data$`2` <- as.character(treasury_data$`2`)
  treasury_data$`2` <- as.numeric(treasury_data$`2`)
treasury_data$`3` <- as.character(treasury_data$`3`)
  treasury_data$`3` <- as.numeric(treasury_data$`3`)
treasury_data$`5` <- as.character(treasury_data$`5`)
  treasury_data$`5` <- as.numeric(treasury_data$`5`)
treasury_data$`7` <- as.character(treasury_data$`7`)
  treasury_data$`7` <- as.numeric(treasury_data$`7`)
treasury_data$`10` <- as.character(treasury_data$`10`)
  treasury_data$`10` <- as.numeric(treasury_data$`10`)
treasury_data$`20` <- as.character(treasury_data$`20`)
  treasury_data$`20` <- as.numeric(treasury_data$`20`)
treasury_data$`30` <- as.character(treasury_data$`30`)
  treasury_data$`30` <- as.numeric(treasury_data$`30`)

# Remove 0 for Term 20 ----
treasury_data <-   
  treasury_data %>%
  filter(`20` != 0)

# Convert Date column to Date ----
treasury_data$Date <- as.character(treasury_data$Date)
treasury_data$Date <- as.Date(treasury_data$Date, format = '%Y-%m-%d')

```


```{r Yield Curve Dates, include=FALSE}

#Current Date (Pulls in last Friday when script is run on Monday) ----
#TODO: Update back to -3 after good Friday
today = Sys.Date()-3

#3 Weeks Ago
Three_Weeks_Ago = today - 21
  print(Three_Weeks_Ago)

#90 Days Ago  ENSURE THAT THE DATE IS A FRIDAY!
Ninety_Days_Ago = today - 90
  print(Ninety_Days_Ago)

#Friday Adjustment
Ninety_Days_Ago = Ninety_Days_Ago - 1

#180 Days Ago  ENSURE THAT THE DATE IS A FRIDAY!
HundredEighty_Days_Ago = today - 180
  print(HundredEighty_Days_Ago)

HundredEighty_Days_Ago = HundredEighty_Days_Ago - 2

#Dates Requires
Dates = c(Three_Weeks_Ago, Ninety_Days_Ago, HundredEighty_Days_Ago)
  Names = c("3_Weeks_Ago", "90_Days_Ago", "180_Days_Ago")

#Data Frame of all Required Dates
Dates_Required = as.data.frame(Dates, Names)
  print(Dates_Required)

```


```{r Creation of Yield Curve Data}

# Current Data ----
tdata_current <-
  treasury_data %>%
  filter(Date == paste(today)) %>%
  mutate(Label = "Current", Position = 1)

# 3 weeks ago ----
tdata_3weeks <-
  treasury_data %>%
  filter(Date == paste(Three_Weeks_Ago)) %>%
  mutate(Label = "3 Weeks Ago", Position = 2)

# 90 Days ago ----
tdata_90days <-
  treasury_data %>%
  filter(Date == paste(Ninety_Days_Ago)) %>%
  mutate(Label = "90 Days Ago", Position = 3)

# 180 Days ago ----
tdata_180days <-
  treasury_data %>%
  filter(Date == paste(HundredEighty_Days_Ago)) %>%
  mutate(Label = "180 Days Ago", Position = 4)

# Year End 2016 ----
tdata_2016 <-
  treasury_data %>%
    filter(Date == '2016-12-30') %>%
    mutate(Label = "12/30/2016", Position = 5)

# Combine into single data frame ----
yc_df <-
  rbind(tdata_current,
        tdata_3weeks,
        tdata_90days,
        tdata_180days,
        tdata_2016)

# Convert to XTS for graphing ----
yc_df_melt <- melt(yc_df, id = c('Position', 'Label'))
yc_df_melt <- yc_df_melt[-(1:5), ]
colnames(yc_df_melt) <- c("Position", "Time", "Term", "Yield")

yc_df_melt$Term = as.character(yc_df_melt$Term)
yc_df_melt$Term = as.numeric(yc_df_melt$Term)

yc_df_melt <-
  yc_df_melt %>%
  arrange(Position)

yc_df_melt <- yc_df_melt[ ,-1]

```


```{r Treasury Yields and Spreads, include=FALSE}

# Creation of xts treasury data object ----
treasury_data_xts <-
  as.xts(treasury_data[, -1], order.by = treasury_data$Date)

# Complete Cases ----
treasury_data_xts_complete <- treasury_data_xts[complete.cases(treasury_data_xts)]

# Treasury Spreads ----
t_spreads <- treasury_data_xts_complete

t_spreads$`2 vs 5` = t_spreads$`5` - t_spreads$`2`
t_spreads$`5 vs 10` = t_spreads$X10 - t_spreads$X5
t_spreads$`10 vs 20` = t_spreads$X20 - t_spreads$X10
t_spreads$`20 vs 30` = t_spreads$X30 - t_spreads$X20

# Removal of original rates ----
t_spreads = t_spreads[, -c(1:7)]
colnames(t_spreads) <- c("2 vs 5", "5 vs 10", "10 vs 20", "20 vs 30")

# Treasury Spread Weekly Difference ----
t_spread_weekly_delta <-
  round(t_spreads - xts::lag.xts(t_spreads, 5) * 100, digits = 2)  

t_spread_weekly_delta <- xts::last(t_spread_weekly_delta)

# YTD Difference ----
t_spread_ye <- t_spreads['2019-01-02']
t_spread_last <- xts::last(t_spreads) # Pulls the last row in the treasury spreads data set.

t_spread_ytd_delta <- rbind(t_spread_ye, t_spread_last)

t_spread_ytd_delta <-
  round(t_spread_ytd_delta - xts::lag.xts(t_spread_ytd_delta) * 100, digits = 2)

t_spread_ytd_delta <- xts::last(t_spread_ytd_delta)


# Spread Table Creation ----
yc_spread_table <- c(t_spread_weekly_delta, t_spread_ytd_delta, t_spread_last)
  datatable(yc_spread_table, colnames = c('2 vs 5','5 vs 10','10 vs 20','20 vs 30'), rownames = c("Weekly Change (BPS)", "YTD (BPS)","Current     Spread"),options = list(bPaginate = FALSE))

```



```{r Corporate Bond Effective Yields Data, include=FALSE}

# Corporate Bond Yields

# CCC or below (rating)
getSymbols.FRED('BAMLH0A3HYCEY',env=.GlobalEnv)

# AAA
getSymbols.FRED('BAMLC0A1CAAAEY',env=.GlobalEnv)

#AA
getSymbols.FRED('BAMLC0A2CAAEY',env=.GlobalEnv)

# A
getSymbols.FRED('BAMLC0A3CAEY',env=.GlobalEnv)

# BBB
getSymbols.FRED('BAMLC0A4CBBBEY',env=.GlobalEnv)

# BB
getSymbols.FRED('BAMLH0A1HYBBEY',env=.GlobalEnv)

# B
getSymbols.FRED('BAMLH0A2HYBEY',env=.GlobalEnv)

# CCC or below (rating)
getSymbols.FRED('BAMLH0A3HYCEY',env=.GlobalEnv)

# Variable Creation
AAA = BAMLC0A1CAAAEY
AA = BAMLC0A2CAAEY
A = BAMLC0A3CAEY
BBB = BAMLC0A4CBBBEY
BB = BAMLH0A1HYBBEY
B = BAMLH0A2HYBEY
CCC = BAMLH0A3HYCEY

# Rename Column
colnames(AAA) <- "AAA"
colnames(AA) <- 'AA'
colnames(A) <- 'A'
colnames(BBB) <- 'BBB'
colnames(BB) <- 'BB'
colnames(B) <- 'B'
colnames(CCC) <- 'CCC'

# Combined XTS Dataframe
CorpBondEY.All <- merge.xts(AAA,AA,A,BBB,BB,B,CCC, all = TRUE)

# Filter NA
NA_Filter <- complete.cases(CorpBondEY.All)

# Removes NA Observations
CorpBondEY.All <- CorpBondEY.All[NA_Filter]

          ################ Data Compilation Complete ####################       

```


```{r Investment Grade Spread (Credit Risk), include=FALSE}

# One method of observing the credit risk spread is to use Moody's Aaa - Baa.  The highest Investment grade yield - Lowest Investment Grade Yield. This will be done in the code below.

#Import data from FRED
getSymbols.FRED('DAAA', env = .GlobalEnv)
getSymbols.FRED('DBAA', env = .GlobalEnv)

#Variable Creation
MAaa <- DAAA
MBaa <- DBAA

#Rename Columns
colnames(MAaa) <- 'Aaa'
colnames(MBaa) <- 'Baa'

#Combined XTS Dataframe
Moodys_InvGrade_Spread <- merge.xts(MAaa, MBaa, all = TRUE)

#Filter NA
MoodysIG_NAFilter <- complete.cases(Moodys_InvGrade_Spread)

#Remove NA Observations
Moodys_InvGrade_Spread <- Moodys_InvGrade_Spread[MoodysIG_NAFilter]

#Creation of Investment Grade Spread Column (loop)
for (i in Moodys_InvGrade_Spread){
Moodys_InvGrade_Spread$Spread <- Moodys_InvGrade_Spread$Baa - Moodys_InvGrade_Spread$Aaa
}

                    ##############################################################################################
              ####### Checkpoint:  Moodys Aaa, Baa, and Investment Grade Spread are now all within the Dataframe ########
                #################################################################################################
```


```{r REIT Index Construction, include=FALSE}

#Market Cap of REITS
CUBE.MarketCap <- "4.92B"
EXR.MarketCap <- "10.9B"
SELF.MarketCap <- "37.26M" #Due to the low market Cap, SELF will be excluded from the index
LSI.MarketCap <- "3.87B"
NSA.MarketCap <- "1.67B"
PSA.MarketCap <- "38.21B"

MarketCap <- c(CUBE.MarketCap, EXR.MarketCap, SELF.MarketCap, LSI.MarketCap, NSA.MarketCap, PSA.MarketCap)
REIT.Names <- c("CUBE", "EXR", "SELF", "LSI", "NSA", "PSA")

MarketCap.DF <- as.data.frame(MarketCap, REIT.Names)

## Data Import ##
getSymbols.yahoo('CUBE',env=.GlobalEnv) #Cubesmart (CUBE)
CUBE <- CUBE[,4]
getSymbols.yahoo('EXR', env = .GlobalEnv) #Extra Space Storage, Inc (EXR)
EXR <- EXR[,4]
getSymbols.yahoo('LSI', env = .GlobalEnv) #Life Storage, Inc (LSI)
LSI <- `LSI`[,4]
getSymbols.yahoo('NSA', env = .GlobalEnv) #National Storage Affiliates (NSA)
NSA <- NSA[,4]
getSymbols.yahoo('PSA', env = .GlobalEnv) #Public Storage (PSA)
PSA <- PSA[,4]

#Merge of REIT Securities
REIT.Securities <- merge.xts(CUBE, EXR, LSI, NSA, PSA)
REIT.Securities.Filter <- complete.cases(REIT.Securities)
REIT.Securities <- REIT.Securities[REIT.Securities.Filter]
REIT.Securities <- REIT.Securities['2015-04-22/']

#Weight Calculation (BOP)
REIT.Securities$Total_Price <- rowSums(REIT.Securities)# Sum of all REIT Stock Prices

#Index Value
REIT.Securities$Index_Value <- REIT.Securities$Total_Price / 5 #Divisor of 5 (number of securities being included in the index)

#Index Return Calculation
REIT.Securities$Percent_Return <- (REIT.Securities$Index_Value / lag(REIT.Securities$Index_Value) - 1) * 100


```



```{r Market Sector ETFs, include=FALSE}

#Financial Sector
getSymbols.yahoo('VFH',env=.GlobalEnv) #Vanguard Financial Sector ETF
Fin.Sec <- VFH[,4] #60
colnames(Fin.Sec) <- 'Financial'

#Utilities Sector
getSymbols.yahoo("VPU", env = .GlobalEnv)
Util.Sec <- VPU[,4]  #121
colnames(Util.Sec) <- 'Utility'

#Consumer Discretionary
getSymbols.yahoo("VCR", env = .GlobalEnv)
ConsDiscr.Sec <- VCR[,4]   #141
colnames(ConsDiscr.Sec) <- 'Cons.Discrete'

#Consumer Staples
getSymbols.yahoo("VDC", env = .GlobalEnv)
ConsStaples.Sec <- VDC[,4] #140
colnames(ConsStaples.Sec) <- 'Cons.Staples'

#Energy
getSymbols.yahoo("VDE", env = .GlobalEnv)
Energy.Sec <- VDE[,4] #87
colnames(Energy.Sec) <- 'Energy'

#Health Care
getSymbols.yahoo("VHT", env = .GlobalEnv)
Health.Sec<- VHT[,4] #153

#Industrials
getSymbols.yahoo("VIS", env = .GlobalEnv)
Industrial.Sec <- VIS[,4] #128
colnames(Industrial.Sec) <- 'Industrial'

#Technology
getSymbols.yahoo("VGT", env = .GlobalEnv)
Tech.Sec <- VGT[,4] #149
colnames(Tech.Sec) <- 'Tech'

#Telecom
getSymbols.yahoo("VOX", env = .GlobalEnv)
Telecom.Sec <- VOX[,4]   #89
colnames(Telecom.Sec) <- 'Telecom'

#Materials
getSymbols.yahoo("VAW", env = .GlobalEnv)
Mat.Sec <- VAW[,4] #124
colnames(Mat.Sec) <- "Materials"

#Repwest Equity Exposure
Repwest.Exposure <- merge.xts(Mat.Sec,ConsDiscr.Sec, ConsStaples.Sec, Fin.Sec, Industrial.Sec, Tech.Sec, Telecom.Sec, Util.Sec)
Repwest.Exposure <- Repwest.Exposure['2016-01-01/']

```


```{r Equity Sector Test, include=FALSE}

dygraph(Repwest.Exposure) %>%
  dyRangeSelector() %>%
    dyHighlight(highlightCircleSize = 5,
                highlightSeriesBackgroundAlpha = .5,
                hideOnMouseOut = FALSE) %>%
    dyAxis("y", label = "NAV") %>%
    dyRoller(rollPeriod = 0) %>%
    dyLegend(width = 700)

```



# Introduction {.sidebar}
=================================
Welcome to the Market Dashboard!  The purpose of this document is to provide a snapshot of market conditions

**Treasury Yield Spreads**

***

|         |2 vs 5   |5 vs 10 |10 vs 20|20 vs 30|
|:-------:|:-------:|:------:|:-----:|:----:|
|W/W (BPS)| -23.80    |-49.53  |-54.46 |-21.77|
|YTD (BPS)|+1.20    |-16.53  |-16.46 |-13.77|
|Current  | 0.20    | 0.47   |0.54   |0.23|
***


**Treasury**
=======================================================================

Row {.tabset .tabset}
-----------------------------------------------------------------------
### **U.S. Treasury Historical Yields (2016 - Current)**

```{r, echo=FALSE}

dygraph(treasury_data_xts_complete) %>%
  dyRangeSelector() %>%
    dyHighlight(highlightCircleSize = 5,
                highlightSeriesBackgroundAlpha = .5,
                hideOnMouseOut = FALSE) %>%
    dyAxis("y", label = "Yield (%)") %>%
    dyRoller(rollPeriod = 0)

```



Row {.tabset .tabset}
-----------------------------------------------------------------------

### **Treasury Spreads (2016 - Current)**

```{r, echo=FALSE}

dygraph(t_spreads) %>%
  dyRangeSelector() %>%
   dyOptions(stackedGraph = FALSE) %>%
   dyHighlight(highlightCircleSize = 5,
              highlightSeriesBackgroundAlpha = 0.5,
              hideOnMouseOut = FALSE) %>%
  dyAxis("y", label = "Yield (%)") %>%
  dyRoller(rollPeriod = 0)

```


### **U.S. Treasury Historical Yield Curve**

```{r}

# Ra-arrange levels ----
yc_df_melt$Time <- as.factor(yc_df_melt$Time)
yc_df_melt$Time <- factor(yc_df_melt$Time, levels = c("Current", "3 Weeks Ago", "90 Days Ago", "180 Days Ago", "12/30/2016"))


YComp = ggplot(data = yc_df_melt, aes(x = Term, y = Yield, color = Time)) +
  geom_line() +
  geom_point() +
  scale_color_brewer(palette = 16) +
  theme_economist() +
  labs(shape = ' ', color = ' ', linetype = ' ') +
  labs(y = 'Yield (%)')

ggplotly(YComp)

```


**Corporate Bonds**
=======================================================================




Row
-----------------------------------------------------------------------

### **BofA Merrill Lynch US Corporate Bond Effective Yields**

```{r BofA Merril Lynch Corporate Bond Yields, echo=FALSE}

  #Source: FRED
  dygraph(CorpBondEY.All["2010/"]) %>%
  dyRangeSelector() %>%
  dyOptions(stackedGraph = FALSE) %>%
   dyHighlight(highlightCircleSize = 5,
              highlightSeriesBackgroundAlpha = 0.5,
              hideOnMouseOut = FALSE) %>%
  dyAxis("y", label = "Yield (%)") %>%
  dyRoller(rollPeriod = 0)

```


Row {.tabset .tabset}
-----------------------------------------------------------------------

### **Moody's Corporate Bond Investment Grade Spread**
```{r}
#Aaa Maturities 20 years and above
#Baa Maturities 20 years and above

  dygraph(Moodys_InvGrade_Spread["2010/"]) %>%
  dyRangeSelector() %>%
    dyHighlight(highlightCircleSize = 5,
                highlightSeriesBackgroundAlpha = .5,
                hideOnMouseOut = FALSE) %>%
    dyAxis("y", label = "Yield (%)") %>%
    dyRoller(rollPeriod = 0)

```


**FED Funds Futures**
=======================================================================


Row
-----------------------------------------------------------------------

### **FED Funds Futures (by Contract)**

```{r Fed Fund Futures contract table, echo=FALSE}

FEDFund.Futures <- read.csv("C:\\Users\\Noe_N\\OneDrive\\Market Data\\market_dashboard\\market_data\\FED Fund Futures.csv", header = TRUE)
datatable(FEDFund.Futures, colnames = c("Meeting Date", "Expires", "Contract", "Price", "Rate"), options = list(bPaginate = FALSE))  
```


Row
-----------------------------------------------------------------------

### **FED Funds Meeting Probabilities**


The table below shows FOMC meeting dates with the probabilities of FED Funds rate ranges (in basis points). The probabilities are calculated and published by the CME Group. For a full description of their methodology, please see the following link:

http://www.cmegroup.com/education/fed-funds-futures-probability-tree-calculator.html


```{r}

FEDfund.meetingProb = read.csv("C:\\Users\\Noe_N\\OneDrive\\Market Data\\market_dashboard\\market_data\\FED Fund Meeting Probabilities.csv", header = TRUE)

datatable(FEDfund.meetingProb, colnames = c("Meeting Date", "0-25"), options = list(bPaginate = FALSE))
```



**FX / SWAPS / Prime**
=======================================================================

Row
-----------------------------------------------------------------------

### **USD/CAD**
```{r, include=FALSE}

# Alternative Yahoo Finance Compilations
USDCAD <- getSymbols("CAD=X", src = "yahoo", auto.assign = FALSE)
USDCAD <- USDCAD[ ,4 ]
USDCAD <- na.omit(USDCAD)
FXStart <- Sys.Date() - 180
FXEnd <- Sys.Date() - 2
USDCAD <- USDCAD["2017-10-11/2019-4-19"]


```

```{r, echo=FALSE}
dygraph(USDCAD) %>%
  dyHighlight(highlightCircleSize = 5,
                highlightSeriesBackgroundAlpha = .5,
                hideOnMouseOut = FALSE) %>%
  dyAxis("y", label = "Exchange Rate") %>%
  dyRoller(rollPeriod = 0) %>%
  dyOptions(sigFigs = 4)

#Ratio of USD to CAD.  1 USD = X CAD

```


Row {.tabset .tabset}
-----------------------------------------------------------------------

### **SWAP Rates: Various Maturities**
```{r Import SWAP Data, echo=FALSE}

# import swaps data ----
swaps <- read.csv("C:\\Users\\Noe_N\\OneDrive\\Market Data\\market_dashboard\\market_data\\Swaps.csv", header = TRUE)

# format data ----
swaps.filter <- complete.cases(swaps)
swaps <- swaps[swaps.filter,]
colnames(swaps) <- c('Date','1 Yr','2 Yr','3 Yr','5 Yr','7 Yr', '10 Yr', '30 Yr')
swaps$Date <- as.Date(swaps$Date, format="%Y-%m-%d")
swaps <- xts(swaps[, c(2:8)], order.by = swaps$Date)
swaps <- swaps['2017-01-19/2020']

# graph swaps ----  
dygraph(swaps) %>%
  dyHighlight(highlightCircleSize = 5,
                highlightSeriesBackgroundAlpha = .5,
                hideOnMouseOut = FALSE) %>%
  dyAxis("y", label = "Rate") %>%
  dyRoller(rollPeriod = 0)


```


### **Prime Rate (2017 - Current)**

```{r}

# Import Prime Rate Data
prime.df <- read.csv("C:\\Users\\Noe_N\\OneDrive\\Market Data\\market_dashboard\\market_data\\Prime.csv", header = TRUE)

# Date Conversion
prime.df$Date <- as.character(prime.df$Date)
prime.df$Date <- as.Date(prime.df$Date, format = "%Y-%m-%d")

# Complete Cases
prime.df <- prime.df[complete.cases(prime.df),]

# xts conversion
prime.xts <- xts(prime.df[,2], order.by = prime.df$Date)

# Subset 2017 - Current
prime.xts <- prime.xts['2017-01-01/']

# Dygraph
    dygraph(prime.xts) %>%
    dyHighlight(highlightCircleSize = 5,
                highlightSeriesBackgroundAlpha = .5,
                hideOnMouseOut = FALSE) %>%
    dyAxis("y", label = "Rate") %>%
    dyRoller(rollPeriod = 0)

```


**Commodities**
=======================================================================

Row
-----------------------------------------------------------------------

### **Mont Belvieu Spot Propane Prices**

```{r}

# Import Belvieu data from FRED ----
spot_propane <- read.csv('C:\\Users\\Noe_N\\OneDrive\\Market Data\\market_dashboard\\market_data\\DPROPANEMBTX.csv')
colnames(spot_propane)[1] <- 'Date'
spot_propane$Date <- as.Date(spot_propane$Date, format = "%m/%d/%Y")

spot_propane <- spot_propane[complete.cases(spot_propane), ]

# xts conversion
spot_propane.xts <- xts(spot_propane[,2], order.by = spot_propane$Date)

# Dygraph
dygraph(spot_propane.xts) %>%
    dyHighlight(highlightCircleSize = 5,
                highlightSeriesBackgroundAlpha = .5,
                hideOnMouseOut = FALSE) %>%
    dyAxis("y", label = "$ Per Gallon") %>%
    dyRoller(rollPeriod = 0)


```




**Summary**
=======================================================================
Row
-----------------------------------------------------------------------

### 10 Year Treasury
```{r, echo=FALSE}

  Current_Treasury <- last(tdata_current[,6])
  valueBox(Current_Treasury,
           icon = "fa-percent")
```

### Moody's IG Spread

```{r}
IGSpread  <- last(round(Moodys_InvGrade_Spread[,3],2))
valueBox(IGSpread,
         icon = 'fa-percent')

```



Row
-----------------------------------------------------------------------

### **U.S. Treasury: Yield Curve**

```{r Treasury Yield Curve, echo=FALSE}

summary_yc <-
  yc_df_melt %>%
  filter(Time == "Current" | Time == "3 Weeks Ago")


YComp <- ggplot(data = summary_yc, aes(x = Term, y = Yield, linetype = Time, shape = Time)) +
  geom_line() +
  geom_point() +
  theme_economist_white() +
  labs(shape = ' ', linetype = ' ') +
  labs(y = 'Yield (%)')

YComp.ggplotly = ggplotly(YComp)  
YComp.ggplotly

```

### **Corporate Bonds: Moody's IG Spread**
```{r, echo=FALSE}

#Weekly Change
Moodys_WeeklyChange <- round((Moodys_InvGrade_Spread - lag.xts(Moodys_InvGrade_Spread, 5)) * 100, digits = 2)
Moodys_WeeklyChange <- xts::last(Moodys_WeeklyChange)

#YTD Change
Moodys_YE <- Moodys_InvGrade_Spread['2020-01-02/']
Moodys_YTDChange <- round((Moodys_YE - lag.xts(Moodys_YE, dim(Moodys_YE)[1] - 1)) * 100, digits = 2)
Moodys_YTDChange <- xts::last(Moodys_YTDChange)

#Current
Moodys_current <- xts::last(Moodys_InvGrade_Spread)
Moodys.Table <- c(Moodys_WeeklyChange, Moodys_YTDChange, round(Moodys_current,2))
Moodys.Table.Final <- datatable(Moodys.Table, colnames = c("Aaa", "Baa", "Spread"),rownames = c("Weekly Change (BPS)", "YTD Change (BPS)","Current (%)"), options <- list(bPaginate = FALSE))
Moodys.Table.Final

```

Row
-----------------------------------------------------------------------

### **Self Storage REIT: Price Weighted Index**
```{r REIT Index, echo=FALSE}

dygraph(REIT.Securities$Index_Value) %>%
  dyHighlight(highlightCircleSize = 5,
              highlightSeriesBackgroundAlpha = .5,
              hideOnMouseOut = FALSE) %>%
  dyAxis("y", label = "Index Value") %>%
  dyRoller(rollPeriod = 0)

```


### **Interest Swaps**
```{r, echo=FALSE}

#Swaps YE
Swap.YE <- swaps['2020-01-02/']

#Weekly Change
Swap.weeklychange <- round(( swaps - lag.xts(swaps, 5)) * 100, digits = 4)
Swap.weeklychange <- xts::last(Swap.weeklychange)

#YTD
Swap.YTD <- round((Swap.YE - lag.xts(Swap.YE, dim(Swap.YE)[1] -1)), digits = 2) * 100  #The lag Number will need to be updated weekly!
Swap.YTD <- xts::last(Swap.YTD)

#Current
Swap.Current <- xts::last(Swap.YE)
Swap.Table <- c(Swap.weeklychange, Swap.YTD, Swap.Current)

#Creation of the Data Table
datatable(Swap.Table, colnames = c('1 Yr','2 Yr','3 Yr','5 Yr','7 Yr','10 Yr','30 Yr'),rownames = c("Weekly Change (BPS)", "YTD (BPS)","Current (%)"), options = list(bPaginate = FALSE))

```

Row
-----------------------------------------------------------------------

### Index Weekly Return (CUBE, EXR, LSI, NSA, and PSA)

```{r}

Index_Week_Return <- round((xts::last(REIT.Securities$Index_Value) / lag.xts(REIT.Securities$Index_Value,4) - 1) * 100,digits = 2)
valueBox(Index_Week_Return,
         icon = 'fa-percent')

```

### 10 Year Swap

```{r}

Swap10yr <- last(swaps[,6])

  valueBox(Swap10yr,
           icon = 'fa-percent', href = NULL)
```

Row
-----------------------------------------------------------------------

### Equity Sector Vanguard ETFs

```{r Equity Markets by Sector}

dygraph(Repwest.Exposure) %>%
  dyRangeSelector() %>%
    dyHighlight(highlightCircleSize = 5,
                highlightSeriesBackgroundAlpha = .5,
                hideOnMouseOut = FALSE) %>%
    dyAxis("y", label = "NAV") %>%
    dyRoller(rollPeriod = 0) %>%
    dyLegend(width = 700)

```

### Equity Sector Returns

```{r, echo=FALSE}

RepW.Weekly <- (Repwest.Exposure / lag.xts(Repwest.Exposure,5) - 1 ) * 100 #Weekly Return (by Sector)
RepW.Weekly <- xts::last(RepW.Weekly)

Repwest.Exposure.YE18 <- Repwest.Exposure['2020-01-02']
RepW.YTD <- xts::last(Repwest.Exposure)
RepW.YTD <- ((coredata(RepW.YTD) / Repwest.Exposure.YE18) - 1) * 100

#Data Frame Compilation
Sector <- colnames(RepW.YTD)
Eq.YTD <- as.numeric(RepW.YTD[1,])
Eq.Weekly <- as.numeric(RepW.Weekly[1,])

Equity.Data <- data.frame(Sector, Eq.YTD, Eq.Weekly) ##Reconcile data to be graphed in a single data frame

#Plot of YTD and Weekly Change
Equity.plot <- Equity.Data %>%
  plot_ly() %>%
    add_trace(x = Sector, y = round(Eq.Weekly, digits = 2), name = 'Weekly Return', type = 'bar',
                text = round(Eq.Weekly, digits = 2) , textposition = 'auto',
                marker = list(color = 'lightskyblue',
                              line = list(color = 'black', width = 1.5))) %>%
    add_trace(x = Sector, y = round(Eq.YTD, digits = 2), name = 'YTD Return', type = 'bar',
                text = round(Eq.YTD, digits = 2), textposition = 'auto',
                marker = list(color = 'rgb(204,204,204)',
                              line = list(color = 'black', width = 1.5))) %>%
    layout(yaxis = list(title = 'Return (%)'))

Equity.plot

```


**Sources**
=======================================================================

* Treasury: https://www.treasury.gov/resource-center/data-chart-center/interest-rates/Pages/TextView.aspx?data=yield
* Corporate Bonds:
     + https://fred.stlouisfed.org/release?rid=209
      + https://fred.stlouisfed.org/search?st=Moody%27s+Seasoned+Aaa+Corporate+Bond+Yield%C2%A9
      + https://fred.stlouisfed.org/search?st=Moody%27s+Seasoned+Baa+Corporate+Bond+Yield%C2%A9

* FED Fund Futures: http://www.cmegroup.com/trading/interest-rates/countdown-to-fomc.html
* FX: https://www.oanda.com/fx-for-business/historical-rates
* Swaps: Reuters (sent by Mason Buckman)
* REIT Index: Google Finance
      + CUBE, EXR, LSI, NSA, and PSA
* Vanguard ETFs: Google Finance
* Questions? Please contact Noe Navarro @ U-Haul Financial Analysis (ext:530305)


```{r publish to shinyapps.io, eval=FALSE, include=FALSE}

rsconnect::setAccountInfo(name='nnavarr',
			  token='78C16A6AA5C5D9DFA299521C1BB5E199',
			  secret='xygEMnefJiNy/RUyFlbZqH1Ik0FF5EyDSiagEoGL')

rsconnect::deployApp()

```